Category: Epidemiology

Summary: Testing whether slower recovery aligned with commuting hubs creates an intermediate recurrence level that maximizes transient epidemic growth.

Commuting systems repeatedly return people to the same hubs, which can interact with local recovery times in ways that do not show up in static-threshold analyses. This experiment asks whether slower recovery at the busiest hubs combines with recurrent hub-focused commuting to create a particularly strong finite-time epidemic growth window.

The simulation sweeps recurrence level while comparing aligned and anti-aligned recovery heterogeneity. The hypothesis is that an interior optimum exists: too little recurrence does not exploit the hub structure, while too much may wash out the transient amplification advantage. Localization diagnostics then track whether the reactive epidemic mode becomes especially hub-focused in that regime.

That isolates an interaction between temporal movement patterns and biological heterogeneity. The result is intended to show whether recovery-time alignment with mobility structure is a distinct control parameter for transient epidemic risk.

Method: GPU-accelerated metapopulation epidemic sweeps over recurrence level with heterogeneous recovery rates aligned or anti-aligned to commuting hubs.

What is measured: Transient-growth window, reactive-mode hub localization, dependence on recurrence level, and effect of aligned versus anti-aligned recovery heterogeneity.